It is often desirable to reduce or otherwise suppress noise in an image. Examples of conventional noise suppression techniques include spatial filtering operations such as Gaussian smoothing, non-linear filtering operations such as median smoothing, and adaptive filtering operations such as Weiner filtering. Such techniques generally produce acceptable results when applied to high-resolution images, such as photographs or other two-dimensional (2D) images produced by a digital camera. However, many important machine vision applications utilize three-dimensional (3D) images generated by depth imagers such as structured light (SL) cameras or time of flight (ToF) cameras. These depth images are often low-resolution images and typically include highly noisy and blurred edges. Application of conventional noise suppression techniques to depth images and other types of low-resolution images can further degrade the quality of the edges present in the images. This can undermine the effectiveness of subsequent image processing operations such as feature extraction, pattern identification, gesture recognition, object recognition and tracking.